Open aditipanda opened 6 years ago
aditipanda
commented
6 years ago In https://github.com/s9xie/hed/issues/14, @yhlleo suggested to change the cuDNN version from v4 to v3. I am using v7. What's the solution? Do I have to downgrade to a lower version of cuDNN?
aditipanda
commented
6 years ago I read the README and it was written that we had to build caffe by using make and make pycaffe commands. I tried doing that, but on running make at M2FCN-master it says: **Makefile:6: * Makefile.config not found. See Makefile.config.example.. Stop.
So I do this first: cp Makefile.config.example Makefile.config
After this I run make but get the following error:
CXX .build_release/src/caffe/proto/caffe.pb.cc In file included from /usr/include/c++/5/mutex:35:0, from /usr/local/include/google/protobuf/stubs/mutex.h:33, from /usr/local/include/google/protobuf/stubs/common.h:52, from .build_release/src/caffe/proto/caffe.pb.h:10, from .build_release/src/caffe/proto/caffe.pb.cc:4: /usr/include/c++/5/bits/c++0x_warning.h:32:2: error: #error This file requires compiler and library support for the ISO C++ 2011 standard. This support must be enabled with the -std=c++11 or -std=gnu++11 compiler options.
^ In file included from /usr/local/include/google/protobuf/stubs/common.h:51:0, from .build_release/src/caffe/proto/caffe.pb.h:10, from .build_release/src/caffe/proto/caffe.pb.cc:4: /usr/local/include/google/protobuf/stubs/logging.h: In function ‘T google::protobuf::internal::CheckNotNull(const char, int, const char, T)’: /usr/local/include/google/protobuf/stubs/logging.h:165:14: error: ‘nullptr’ was not declared in this scope if (val == nullptr) { ^ In file included from /usr/local/include/google/protobuf/stubs/common.h:52:0, from .build_release/src/caffe/proto/caffe.pb.h:10, from .buildrelease/src/caffe/proto/caffe.pb.cc:4: /usr/local/include/google/protobuf/stubs/mutex.h: At global scope: /usr/local/include/google/protobuf/stubs/mutex.h:70:8: error: ‘mutex’ in namespace ‘std’ does not name a type std::mutex mu; ^ /usr/local/include/google/protobuf/stubs/mutex.h: In member function ‘void google::protobuf::internal::WrappedMutex::Lock()’: /usr/local/include/google/protobuf/stubs/mutex.h:63:43: error: ‘mu_’ was not declared in this scope void Lock() GOOGLE_PROTOBUFACQUIRE() { mu.lock(); } ^ /usr/local/include/google/protobuf/stubs/mutex.h: In member function ‘void google::protobuf::internal::WrappedMutex::Unlock()’: /usr/local/include/google/protobuf/stubs/mutex.h:64:45: error: ‘mu_’ was not declared in this scope void Unlock() GOOGLE_PROTOBUFRELEASE() { mu.unlock(); } ^ /usr/local/include/google/protobuf/stubs/mutex.h: At global scope: /usr/local/include/google/protobuf/stubs/mutex.h:73:7: error: expected nested-name-specifier before ‘Mutex’ using Mutex = WrappedMutex; ^ /usr/local/include/google/protobuf/stubs/mutex.h:78:28: error: expected ‘)’ before ‘’ token explicit MutexLock(Mutex mu) : mu(mu) { this->mu->Lock(); } ^ /usr/local/include/google/protobuf/stubs/mutex.h:81:3: error: ‘Mutex’ does not name a type Mutex const mu; ^ /usr/local/include/google/protobuf/stubs/mutex.h: In destructor ‘google::protobuf::internal::MutexLock::~MutexLock()’: /usr/local/include/google/protobuf/stubs/mutex.h:79:24: error: ‘class google::protobuf::internal::MutexLock’ has no member named ‘mu’ ~MutexLock() { this->mu_->Unlock(); } ^ /usr/local/include/google/protobuf/stubs/mutex.h: At global scope: /usr/local/include/google/protobuf/stubs/mutex.h:92:33: error: expected ‘)’ before ‘’ token explicit MutexLockMaybe(Mutex *mu) : ^ In file included from /usr/local/include/google/protobuf/arena.h:48:0, from .build_release/src/caffe/proto/caffe.pb.h:23, from .build_release/src/caffe/proto/caffe.pb.cc:4: /usr/include/c++/5/typeinfo:39:37: error: expected ‘}’ before end of line /usr/include/c++/5/typeinfo:39:37: error: expected unqualified-id before end of line /usr/include/c++/5/typeinfo:39:37: error: expected ‘}’ before end of line /usr/include/c++/5/typeinfo:39:37: error: expected ‘}’ before end of line /usr/include/c++/5/typeinfo:39:37: error: expected ‘}’ before end of line /usr/include/c++/5/typeinfo:39:37: error: expected declaration before end of line Makefile:570: recipe for target '.build_release/src/caffe/proto/caffe.pb.o' failed make: *** [.build_release/src/caffe/proto/caffe.pb.o] Error 1
yongshuaihuang
commented
4 years ago hi，have you solved this probelm?
I have been trying to run M2FCN (https://github.com/BinWang-shu/M2FCN), but I get these errors. Caffe builds successfully. No errors. The error on running ./examples/final_results/solve.py:
WARNING: Logging before InitGoogleLogging() is written to STDERR I0828 21:43:52.592820 11647 solver.cpp:45] Initializing solver from parameters: base_lr: 1e-09 display: 20 max_iter: 30001 lr_policy: "step" gamma: 0.1 momentum: 0.9 weight_decay: 0.0002 stepsize: 25000 snapshot: 1000 snapshot_prefix: "hed_R" net: "train_val_R.prototxt" iter_size: 10 I0828 21:43:52.592905 11647 solver.cpp:102] Creating training net from net file: train_val_R.prototxt I0828 21:43:52.593868 11647 net.cpp:53] Initializing net from parameters: name: "HED" state { phase: TRAIN } layer { name: "data" type: "ImageLabelmapData" top: "data" top: "label" include { phase: TRAIN } transform_param { mirror: false mean_value: 104.006989 mean_value: 116.66877 mean_value: 122.678917 } image_data_param { source: "/media/data1/JY/neuron/ISBI/ISBI_train/train_with_def_20/train_pair.txt" batch_size: 1 shuffle: true new_height: 0 new_width: 0 root_folder: "" } } layer { name: "conv1_1" type: "Convolution" bottom: "data" top: "conv1_1" param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 64 pad: 35 kernel_size: 3 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "relu1_1" type: "ReLU" bottom: "conv1_1" top: "conv1_1" } layer { name: "conv1_2" type: "Convolution" bottom: "conv1_1" top: "conv1_2" param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 64 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "relu1_2" type: "ReLU" bottom: "conv1_2" top: "conv1_2" } layer { name: "pool1" type: "Pooling" bottom: "conv1_2" top: "pool1" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv2_1" type: "Convolution" bottom: "pool1" top: "conv2_1" param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 128 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "relu2_1" type: "ReLU" bottom: "conv2_1" top: "conv2_1" } layer { name: "conv2_2" type: "Convolution" bottom: "conv2_1" top: "conv2_2" param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 128 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "relu2_2" type: "ReLU" bottom: "conv2_2" top: "conv2_2" } layer { name: "pool2" type: "Pooling" bottom: "conv2_2" top: "pool2" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv3_1" type: "Convolution" bottom: "pool2" top: "conv3_1" param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "relu3_1" type: "ReLU" bottom: "conv3_1" top: "conv3_1" } layer { name: "conv3_2" type: "Convolution" bottom: "conv3_1" top: "conv3_2" param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "relu3_2" type: "ReLU" bottom: "conv3_2" top: "conv3_2" } layer { name: "conv3_3" type: "Convolution" bottom: "conv3_2" top: "conv3_3" param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "relu3_3" type: "ReLU" bottom: "conv3_3" top: "conv3_3" } layer { name: "pool3" type: "Pooling" bottom: "conv3_3" top: "pool3" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv4_1" type: "Convolution" bottom: "pool3" top: "conv4_1" param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "relu4_1" type: "ReLU" bottom: "conv4_1" top: "conv4_1" } layer { name: "conv4_2" type: "Convolution" bottom: "conv4_1" top: "conv4_2" param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "relu4_2" type: "ReLU" bottom: "conv4_2" top: "conv4_2" } layer { name: "conv4_3" type: "Convolution" bottom: "conv4_2" top: "conv4_3" param { lr_mult: 0.1 decay_mult: 1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "relu4_3" type: "ReLU" bottom: "conv4_3" top: "conv4_3" } layer { name: "pool4" type: "Pooling" bottom: "conv4_3" top: "pool4" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv5_1" type: "Convolution" bottom: "pool4" top: "conv5_1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "relu5_1" type: "ReLU" bottom: "conv5_1" top: "conv5_1" } layer { name: "conv5_2" type: "Convolution" bottom: "conv5_1" top: "conv5_2" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "relu5_2" type: "ReLU" bottom: "conv5_2" top: "conv5_2" } layer { name: "conv5_3" type: "Convolution" bottom: "conv5_2" top: "conv5_3" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "relu5_3" type: "ReLU" bottom: "conv5_3" top: "conv5_3" } layer { name: "score-dsn1" type: "Convolution" bottom: "conv1_2" top: "score-dsn1-up" param { lr_mult: 0.01 decay_mult: 1 } param { lr_mult: 0.02 decay_mult: 0 } convolution_param { num_output: 1 kernel_size: 1 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "crop" type: "Crop" bottom: "score-dsn1-up" bottom: "data" top: "upscore-dsn1" } layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn1" bottom: "label" top: "dsn1_loss" loss_weight: 1 } layer { name: "score-dsn2" type: "Convolution" bottom: "conv2_2" top: "score-dsn2" param { lr_mult: 0.01 decay_mult: 1 } param { lr_mult: 0.02 decay_mult: 0 } convolution_param { num_output: 1 kernel_size: 1 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "upsample_2" type: "Deconvolution" bottom: "score-dsn2" top: "score-dsn2-up" param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0 } convolution_param { num_output: 1 kernel_size: 4 stride: 2 } } layer { name: "crop" type: "Crop" bottom: "score-dsn2-up" bottom: "data" top: "upscore-dsn2" } layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn2" bottom: "label" top: "dsn2_loss" loss_weight: 1 } layer { name: "score-dsn3" type: "Convolution" bottom: "conv3_3" top: "score-dsn3" param { lr_mult: 0.01 decay_mult: 1 } param { lr_mult: 0.02 decay_mult: 0 } convolution_param { num_output: 1 kernel_size: 1 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "upsample_4" type: "Deconvolution" bottom: "score-dsn3" top: "score-dsn3-up" param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0 } convolution_param { num_output: 1 kernel_size: 8 stride: 4 } } layer { name: "crop" type: "Crop" bottom: "score-dsn3-up" bottom: "data" top: "upscore-dsn3" } layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn3" bottom: "label" top: "dsn3_loss" loss_weight: 1 } layer { name: "score-dsn4" type: "Convolution" bottom: "conv4_3" top: "score-dsn4" param { lr_mult: 0.01 decay_mult: 1 } param { lr_mult: 0.02 decay_mult: 0 } convolution_param { num_output: 1 kernel_size: 1 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "upsample_8" type: "Deconvolution" bottom: "score-dsn4" top: "score-dsn4-up" param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0 } convolution_param { num_output: 1 kernel_size: 16 stride: 8 } } layer { name: "crop" type: "Crop" bottom: "score-dsn4-up" bottom: "data" top: "upscore-dsn4" } layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn4" bottom: "label" top: "dsn4_loss" loss_weight: 1 } layer { name: "score-dsn5" type: "Convolution" bottom: "conv5_3" top: "score-dsn5" param { lr_mult: 0.01 decay_mult: 1 } param { lr_mult: 0.02 decay_mult: 0 } convolution_param { num_output: 1 kernel_size: 1 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "upsample_16" type: "Deconvolution" bottom: "score-dsn5" top: "score-dsn5-up" param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0 } convolution_param { num_output: 1 kernel_size: 32 stride: 16 } } layer { name: "crop" type: "Crop" bottom: "score-dsn5-up" bottom: "data" top: "upscore-dsn5" } layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn5" bottom: "label" top: "dsn5_loss" loss_weight: 1 } layer { name: "concat" type: "Concat" bottom: "upscore-dsn1" bottom: "upscore-dsn2" bottom: "upscore-dsn3" bottom: "upscore-dsn4" bottom: "upscore-dsn5" top: "concat-upscore" concat_param { concat_dim: 1 } } layer { name: "new-score-weighting" type: "Convolution" bottom: "concat-upscore" top: "upscore-fuse" param { lr_mult: 0.001 decay_mult: 1 } param { lr_mult: 0.002 decay_mult: 0 } convolution_param { num_output: 1 kernel_size: 1 weight_filler { type: "constant" value: 0.2 } engine: CAFFE } } layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-fuse" bottom: "label" top: "fuse_loss" loss_weight: 1 } layer { name: "concat_m" type: "Concat" bottom: "data" bottom: "upscore-dsn1" bottom: "upscore-dsn2" bottom: "upscore-dsn3" bottom: "upscore-dsn4" bottom: "upscore-dsn5" bottom: "upscore-fuse" top: "concat-upscore-m" concat_param { concat_dim: 1 } } layer { name: "conv1_1_R" type: "Convolution" bottom: "concat-upscore-m" top: "conv1_1_R" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 64 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "relu1_1_R" type: "ReLU" bottom: "conv1_1_R" top: "conv1_1_R" } layer { name: "conv1_2_R" type: "Convolution" bottom: "conv1_1_R" top: "conv1_2_R" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 64 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "relu1_2_R" type: "ReLU" bottom: "conv1_2_R" top: "conv1_2_R" } layer { name: "pool1_R" type: "Pooling" bottom: "conv1_2_R" top: "pool1_R" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv2_1_R" type: "Convolution" bottom: "pool1_R" top: "conv2_1_R" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 128 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "relu2_1_R" type: "ReLU" bottom: "conv2_1_R" top: "conv2_1_R" } layer { name: "conv2_2_R" type: "Convolution" bottom: "conv2_1_R" top: "conv2_2_R" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 128 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "relu2_2_R" type: "ReLU" bottom: "conv2_2_R" top: "conv2_2_R" } layer { name: "pool2_R" type: "Pooling" bottom: "conv2_2_R" top: "pool2_R" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv3_1_R" type: "Convolution" bottom: "pool2_R" top: "conv3_1_R" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "relu3_1_R" type: "ReLU" bottom: "conv3_1_R" top: "conv3_1_R" } layer { name: "conv3_2_R" type: "Convolution" bottom: "conv3_1_R" top: "conv3_2_R" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "relu3_2_R" type: "ReLU" bottom: "conv3_2_R" top: "conv3_2_R" } layer { name: "conv3_3_R" type: "Convolution" bottom: "conv3_2_R" top: "conv3_3_R" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "relu3_3_R" type: "ReLU" bottom: "conv3_3_R" top: "conv3_3_R" } layer { name: "pool3_R" type: "Pooling" bottom: "conv3_3_R" top: "pool3_R" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv4_1_R" type: "Convolution" bottom: "pool3_R" top: "conv4_1_R" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "relu4_1_R" type: "ReLU" bottom: "conv4_1_R" top: "conv4_1_R" } layer { name: "conv4_2_R" type: "Convolution" bottom: "conv4_1_R" top: "conv4_2_R" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "relu4_2_R" type: "ReLU" bottom: "conv4_2_R" top: "conv4_2_R" } layer { name: "conv4_3_R" type: "Convolution" bottom: "conv4_2_R" top: "conv4_3_R" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "relu4_3_R" type: "ReLU" bottom: "conv4_3_R" top: "conv4_3_R" } layer { name: "pool4_R" type: "Pooling" bottom: "conv4_3_R" top: "pool4_R" pooling_param { pool: MAX kernel_size: 2 stride: 2 } } layer { name: "conv5_1_R" type: "Convolution" bottom: "pool4_R" top: "conv5_1_R" param { lr_mult: 100 decay_mult: 1 } param { lr_mult: 200 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "relu5_1_R" type: "ReLU" bottom: "conv5_1_R" top: "conv5_1_R" } layer { name: "conv5_2_R" type: "Convolution" bottom: "conv5_1_R" top: "conv5_2_R" param { lr_mult: 100 decay_mult: 1 } param { lr_mult: 200 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "relu5_2_R" type: "ReLU" bottom: "conv5_2_R" top: "conv5_2_R" } layer { name: "conv5_3_R" type: "Convolution" bottom: "conv5_2_R" top: "conv5_3_R" param { lr_mult: 100 decay_mult: 1 } param { lr_mult: 200 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "relu5_3_R" type: "ReLU" bottom: "conv5_3_R" top: "conv5_3_R" } layer { name: "score-dsn1_R" type: "Convolution" bottom: "conv1_2_R" top: "score-dsn1-up_R" param { lr_mult: 0.01 decay_mult: 1 } param { lr_mult: 0.02 decay_mult: 0 } convolution_param { num_output: 1 kernel_size: 1 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "crop_R" type: "Crop" bottom: "score-dsn1-up_R" bottom: "data" top: "upscore-dsn1_R" } layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn1_R" bottom: "label" top: "dsn1_loss_R" loss_weight: 1 } layer { name: "score-dsn2_R" type: "Convolution" bottom: "conv2_2_R" top: "score-dsn2_R" param { lr_mult: 0.01 decay_mult: 1 } param { lr_mult: 0.02 decay_mult: 0 } convolution_param { num_output: 1 kernel_size: 1 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "upsample_2_R" type: "Deconvolution" bottom: "score-dsn2_R" top: "score-dsn2-up_R" param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0 } convolution_param { num_output: 1 kernel_size: 4 stride: 2 } } layer { name: "crop_R" type: "Crop" bottom: "score-dsn2-up_R" bottom: "data" top: "upscore-dsn2_R" } layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn2_R" bottom: "label" top: "dsn2_loss_R" loss_weight: 1 } layer { name: "score-dsn3_R" type: "Convolution" bottom: "conv3_3_R" top: "score-dsn3_R" param { lr_mult: 0.01 decay_mult: 1 } param { lr_mult: 0.02 decay_mult: 0 } convolution_param { num_output: 1 kernel_size: 1 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "upsample_4_R" type: "Deconvolution" bottom: "score-dsn3_R" top: "score-dsn3-up_R" param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0 } convolution_param { num_output: 1 kernel_size: 8 stride: 4 } } layer { name: "crop_R" type: "Crop" bottom: "score-dsn3-up_R" bottom: "data" top: "upscore-dsn3_R" } layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn3_R" bottom: "label" top: "dsn3_loss_R" loss_weight: 1 } layer { name: "score-dsn4_R" type: "Convolution" bottom: "conv4_3_R" top: "score-dsn4_R" param { lr_mult: 0.01 decay_mult: 1 } param { lr_mult: 0.02 decay_mult: 0 } convolution_param { num_output: 1 kernel_size: 1 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "upsample_8_R" type: "Deconvolution" bottom: "score-dsn4_R" top: "score-dsn4-up_R" param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0 } convolution_param { num_output: 1 kernel_size: 16 stride: 8 } } layer { name: "crop_R" type: "Crop" bottom: "score-dsn4-up_R" bottom: "data" top: "upscore-dsn4_R" } layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn4_R" bottom: "label" top: "dsn4_loss_R" loss_weight: 1 } layer { name: "score-dsn5_R" type: "Convolution" bottom: "conv5_3_R" top: "score-dsn5_R" param { lr_mult: 0.01 decay_mult: 1 } param { lr_mult: 0.02 decay_mult: 0 } convolution_param { num_output: 1 kernel_size: 1 weight_filler { type: "xavier" } engine: CAFFE } } layer { name: "upsample_16_R" type: "Deconvolution" bottom: "score-dsn5_R" top: "score-dsn5-up_R" param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0 } convolution_param { num_output: 1 kernel_size: 32 stride: 16 } } layer { name: "crop_R" type: "Crop" bottom: "score-dsn5-up_R" bottom: "data" top: "upscore-dsn5_R" } layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn5_R" bottom: "label" top: "dsn5_loss_R" loss_weight: 1 } layer { name: "concat_R" type: "Concat" bottom: "upscore-dsn1_R" bottom: "upscore-dsn2_R" bottom: "upscore-dsn3_R" bottom: "upscore-dsn4_R" bottom: "upscore-dsn5_R" top: "concat-upscore_R" concat_param { concat_dim: 1 } } layer { name: "new-score-weighting_R" type: "Convolution" bottom: "concat-upscore_R" top: "upscore-fuse_R" param { lr_mult: 0.001 decay_mult: 1 } param { lr_mult: 0.002 decay_mult: 0 } convolution_param { num_output: 1 kernel_size: 1 weight_filler { type: "constant" value: 0.2 } engine: CAFFE } } layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-fuse_R" bottom: "label" top: "fuse_loss_R" loss_weight: 1 } layer { name: "concat_F" type: "Concat" bottom: "upscore-fuse" bottom: "upscore-fuse_R" top: "concat-upscore_F" concat_param { concat_dim: 1 } } layer { name: "new-score-weighting_F" type: "Convolution" bottom: "concat-upscore_F" top: "upscore-fuse_F" param { lr_mult: 0.001 decay_mult: 1 } param { lr_mult: 0.002 decay_mult: 0 } convolution_param { num_output: 1 kernel_size: 1 weight_filler { type: "constant" value: 0.5 } engine: CAFFE } } layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-fuse_F" bottom: "label" top: "fuse_loss_F" loss_weight: 1 } I0828 21:43:52.595218 11647 layer_factory.hpp:77] Creating layer data F0828 21:43:52.595238 11647 layer_factory.hpp:81] Check failed: registry.count(type) == 1 (0 vs. 1) Unknown layer type: ImageLabelmapData (known types: AbsVal, Accuracy, ArgMax, BNLL, BatchNorm, BatchReindex, Bias, Clip, Concat, ContrastiveLoss, Convolution, Crop, Data, Deconvolution, Dropout, DummyData, ELU, Eltwise, Embed, EuclideanLoss, Exp, Filter, Flatten, HDF5Data, HDF5Output, HingeLoss, Im2col, ImageData, InfogainLoss, InnerProduct, Input, LRN, LSTM, LSTMUnit, Log, MVN, MemoryData, MultinomialLogisticLoss, PReLU, Parameter, Pooling, Power, Python, RNN, ReLU, Reduction, Reshape, SPP, Scale, Sigmoid, SigmoidCrossEntropyLoss, Silence, Slice, Softmax, SoftmaxWithLoss, Split, Swish, TanH, Threshold, Tile, WindowData) ** Check failure stack trace: Aborted (core dumped)**